Drift Monitoring as Service for MLOps

Even minor changes in data distribution can significantly impact the performance of machine learning models, rendering them less reliable as data evolves.
Drift detectors are tools specifically designed as a measure to prevent such degradation in model performance by identifying data and concept drifts. Further
enhancement of drift detectors with robust monitoring and alerting mechanisms is crucial to help data scientists detect and address these changes in data in a
timely manner. Hereby, we describe our development of a drift monitoring system aimed at supporting consistent model performance and improving the
overall reliability of AI/ML models over time.